Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations30952
Missing cells52
Missing cells (%)< 0.1%
Duplicate rows3708
Duplicate rows (%)12.0%
Total size in memory12.3 MiB
Average record size in memory416.5 B

Variable types

Numeric20
Categorical4

Alerts

Dataset has 3708 (12.0%) duplicate rowsDuplicates
bill_amt1 is highly overall correlated with bill_amt2 and 4 other fieldsHigh correlation
bill_amt2 is highly overall correlated with bill_amt1 and 5 other fieldsHigh correlation
bill_amt3 is highly overall correlated with bill_amt1 and 5 other fieldsHigh correlation
bill_amt4 is highly overall correlated with bill_amt1 and 5 other fieldsHigh correlation
bill_amt5 is highly overall correlated with bill_amt1 and 5 other fieldsHigh correlation
bill_amt6 is highly overall correlated with bill_amt1 and 6 other fieldsHigh correlation
pay_1 is highly overall correlated with pay_2High correlation
pay_2 is highly overall correlated with pay_1 and 1 other fieldsHigh correlation
pay_3 is highly overall correlated with pay_2 and 1 other fieldsHigh correlation
pay_4 is highly overall correlated with pay_3 and 2 other fieldsHigh correlation
pay_5 is highly overall correlated with pay_4 and 1 other fieldsHigh correlation
pay_6 is highly overall correlated with pay_4 and 1 other fieldsHigh correlation
pay_amt1 is highly overall correlated with bill_amt2High correlation
pay_amt2 is highly overall correlated with bill_amt3High correlation
pay_amt3 is highly overall correlated with bill_amt4High correlation
pay_amt4 is highly overall correlated with bill_amt5 and 2 other fieldsHigh correlation
pay_amt5 is highly overall correlated with bill_amt6High correlation
pay_amt6 is highly overall correlated with pay_amt4High correlation
pay_amt2 is highly skewed (γ1 = 20.0717412)Skewed
pay_1 has 19548 (63.2%) zerosZeros
pay_2 has 21800 (70.4%) zerosZeros
pay_3 has 22831 (73.8%) zerosZeros
pay_4 has 24418 (78.9%) zerosZeros
pay_5 has 25294 (81.7%) zerosZeros
pay_6 has 25237 (81.5%) zerosZeros
bill_amt1 has 1827 (5.9%) zerosZeros
bill_amt2 has 2179 (7.0%) zerosZeros
bill_amt3 has 2526 (8.2%) zerosZeros
bill_amt4 has 2804 (9.1%) zerosZeros
bill_amt5 has 3129 (10.1%) zerosZeros
bill_amt6 has 3717 (12.0%) zerosZeros
pay_amt1 has 6253 (20.2%) zerosZeros
pay_amt2 has 6068 (19.6%) zerosZeros
pay_amt3 has 6493 (21.0%) zerosZeros
pay_amt4 has 6870 (22.2%) zerosZeros
pay_amt5 has 7098 (22.9%) zerosZeros
pay_amt6 has 7678 (24.8%) zerosZeros

Reproduction

Analysis started2024-07-30 13:12:29.350462
Analysis finished2024-07-30 13:13:21.533538
Duration52.18 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

limit_bal
Real number (ℝ)

Distinct79
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145680.65
Minimum10000
Maximum800000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:21.642348image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median100000
Q3210000
95-th percentile400000
Maximum800000
Range790000
Interquartile range (IQR)160000

Descriptive statistics

Standard deviation125010.78
Coefficient of variation (CV)0.85811522
Kurtosis1.1373605
Mean145680.65
Median Absolute Deviation (MAD)70000
Skewness1.237001
Sum4.5091074 × 109
Variance1.5627695 × 1010
MonotonicityNot monotonic
2024-07-30T20:13:21.910657image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 4048
 
13.1%
20000 2697
 
8.7%
30000 2256
 
7.3%
80000 1678
 
5.4%
200000 1379
 
4.5%
100000 1240
 
4.0%
150000 1028
 
3.3%
60000 985
 
3.2%
70000 892
 
2.9%
180000 824
 
2.7%
Other values (69) 13925
45.0%
ValueCountFrequency (%)
10000 734
 
2.4%
16000 2
 
< 0.1%
20000 2697
8.7%
30000 2256
7.3%
40000 363
 
1.2%
50000 4048
13.1%
60000 985
 
3.2%
70000 892
 
2.9%
80000 1678
5.4%
90000 752
 
2.4%
ValueCountFrequency (%)
800000 2
 
< 0.1%
780000 2
 
< 0.1%
750000 4
< 0.1%
740000 1
 
< 0.1%
730000 2
 
< 0.1%
720000 2
 
< 0.1%
710000 7
< 0.1%
700000 8
< 0.1%
690000 1
 
< 0.1%
680000 2
 
< 0.1%

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Wanita
18274 
Pria
12678 

Length

Max length6
Median length6
Mean length5.1807961
Min length4

Characters and Unicode

Total characters160356
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWanita
2nd rowPria
3rd rowWanita
4th rowWanita
5th rowPria

Common Values

ValueCountFrequency (%)
Wanita 18274
59.0%
Pria 12678
41.0%

Length

2024-07-30T20:13:22.095413image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-30T20:13:22.237347image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
wanita 18274
59.0%
pria 12678
41.0%

Most occurring characters

ValueCountFrequency (%)
a 49226
30.7%
i 30952
19.3%
W 18274
 
11.4%
n 18274
 
11.4%
t 18274
 
11.4%
P 12678
 
7.9%
r 12678
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 160356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 49226
30.7%
i 30952
19.3%
W 18274
 
11.4%
n 18274
 
11.4%
t 18274
 
11.4%
P 12678
 
7.9%
r 12678
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 160356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 49226
30.7%
i 30952
19.3%
W 18274
 
11.4%
n 18274
 
11.4%
t 18274
 
11.4%
P 12678
 
7.9%
r 12678
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 160356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 49226
30.7%
i 30952
19.3%
W 18274
 
11.4%
n 18274
 
11.4%
t 18274
 
11.4%
P 12678
 
7.9%
r 12678
 
7.9%

education
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Sarjana
15105 
Pascasarjana
9960 
SMA
5525 
Lainya
 
362

Length

Max length12
Median length7
Mean length7.8832386
Min length3

Characters and Unicode

Total characters244002
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSMA
2nd rowSMA
3rd rowPascasarjana
4th rowSarjana
5th rowSarjana

Common Values

ValueCountFrequency (%)
Sarjana 15105
48.8%
Pascasarjana 9960
32.2%
SMA 5525
 
17.9%
Lainya 362
 
1.2%

Length

2024-07-30T20:13:22.361944image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-30T20:13:22.476384image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
sarjana 15105
48.8%
pascasarjana 9960
32.2%
sma 5525
 
17.9%
lainya 362
 
1.2%

Most occurring characters

ValueCountFrequency (%)
a 95839
39.3%
n 25427
 
10.4%
r 25065
 
10.3%
j 25065
 
10.3%
S 20630
 
8.5%
s 19920
 
8.2%
P 9960
 
4.1%
c 9960
 
4.1%
M 5525
 
2.3%
A 5525
 
2.3%
Other values (3) 1086
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 244002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 95839
39.3%
n 25427
 
10.4%
r 25065
 
10.3%
j 25065
 
10.3%
S 20630
 
8.5%
s 19920
 
8.2%
P 9960
 
4.1%
c 9960
 
4.1%
M 5525
 
2.3%
A 5525
 
2.3%
Other values (3) 1086
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 244002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 95839
39.3%
n 25427
 
10.4%
r 25065
 
10.3%
j 25065
 
10.3%
S 20630
 
8.5%
s 19920
 
8.2%
P 9960
 
4.1%
c 9960
 
4.1%
M 5525
 
2.3%
A 5525
 
2.3%
Other values (3) 1086
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 244002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 95839
39.3%
n 25427
 
10.4%
r 25065
 
10.3%
j 25065
 
10.3%
S 20630
 
8.5%
s 19920
 
8.2%
P 9960
 
4.1%
c 9960
 
4.1%
M 5525
 
2.3%
A 5525
 
2.3%
Other values (3) 1086
 
0.4%

marriage
Categorical

Distinct3
Distinct (%)< 0.1%
Missing52
Missing (%)0.2%
Memory size2.1 MiB
Lajang
16392 
Menikah
14131 
Lainya
 
377

Length

Max length7
Median length6
Mean length6.4573139
Min length6

Characters and Unicode

Total characters199531
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLajang
2nd rowLajang
3rd rowLajang
4th rowMenikah
5th rowMenikah

Common Values

ValueCountFrequency (%)
Lajang 16392
53.0%
Menikah 14131
45.7%
Lainya 377
 
1.2%
(Missing) 52
 
0.2%

Length

2024-07-30T20:13:22.607560image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-30T20:13:22.719155image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
lajang 16392
53.0%
menikah 14131
45.7%
lainya 377
 
1.2%

Most occurring characters

ValueCountFrequency (%)
a 47669
23.9%
n 30900
15.5%
L 16769
 
8.4%
j 16392
 
8.2%
g 16392
 
8.2%
i 14508
 
7.3%
M 14131
 
7.1%
e 14131
 
7.1%
k 14131
 
7.1%
h 14131
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 199531
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 47669
23.9%
n 30900
15.5%
L 16769
 
8.4%
j 16392
 
8.2%
g 16392
 
8.2%
i 14508
 
7.3%
M 14131
 
7.1%
e 14131
 
7.1%
k 14131
 
7.1%
h 14131
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 199531
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 47669
23.9%
n 30900
15.5%
L 16769
 
8.4%
j 16392
 
8.2%
g 16392
 
8.2%
i 14508
 
7.3%
M 14131
 
7.1%
e 14131
 
7.1%
k 14131
 
7.1%
h 14131
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 199531
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 47669
23.9%
n 30900
15.5%
L 16769
 
8.4%
j 16392
 
8.2%
g 16392
 
8.2%
i 14508
 
7.3%
M 14131
 
7.1%
e 14131
 
7.1%
k 14131
 
7.1%
h 14131
 
7.1%

age
Real number (ℝ)

Distinct55
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.321336
Minimum21
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:22.855309image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q342
95-th percentile53
Maximum75
Range54
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.3758775
Coefficient of variation (CV)0.26544515
Kurtosis-0.10238633
Mean35.321336
Median Absolute Deviation (MAD)7
Skewness0.70291584
Sum1093266
Variance87.907079
MonotonicityNot monotonic
2024-07-30T20:13:23.010408image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 1548
 
5.0%
27 1533
 
5.0%
28 1435
 
4.6%
25 1398
 
4.5%
30 1375
 
4.4%
26 1349
 
4.4%
24 1333
 
4.3%
32 1186
 
3.8%
31 1159
 
3.7%
35 1118
 
3.6%
Other values (45) 17518
56.6%
ValueCountFrequency (%)
21 72
 
0.2%
22 707
2.3%
23 1104
3.6%
24 1333
4.3%
25 1398
4.5%
26 1349
4.4%
27 1533
5.0%
28 1435
4.6%
29 1548
5.0%
30 1375
4.4%
ValueCountFrequency (%)
75 1
 
< 0.1%
74 1
 
< 0.1%
73 2
 
< 0.1%
72 3
 
< 0.1%
71 1
 
< 0.1%
70 16
0.1%
69 11
 
< 0.1%
68 5
 
< 0.1%
67 13
< 0.1%
66 32
0.1%

pay_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56219307
Minimum0
Maximum8
Zeros19548
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:23.135547image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8628677
Coefficient of variation (CV)1.5348245
Kurtosis6.4885528
Mean0.56219307
Median Absolute Deviation (MAD)0
Skewness1.9019993
Sum17401
Variance0.74454067
MonotonicityNot monotonic
2024-07-30T20:13:23.267850image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 19548
63.2%
1 6402
 
20.7%
2 4366
 
14.1%
3 456
 
1.5%
4 105
 
0.3%
5 27
 
0.1%
8 23
 
0.1%
6 15
 
< 0.1%
7 10
 
< 0.1%
ValueCountFrequency (%)
0 19548
63.2%
1 6402
 
20.7%
2 4366
 
14.1%
3 456
 
1.5%
4 105
 
0.3%
5 27
 
0.1%
6 15
 
< 0.1%
7 10
 
< 0.1%
8 23
 
0.1%
ValueCountFrequency (%)
8 23
 
0.1%
7 10
 
< 0.1%
6 15
 
< 0.1%
5 27
 
0.1%
4 105
 
0.3%
3 456
 
1.5%
2 4366
 
14.1%
1 6402
 
20.7%
0 19548
63.2%

pay_2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63669553
Minimum0
Maximum8
Zeros21800
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:23.392797image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0284261
Coefficient of variation (CV)1.6152557
Kurtosis1.9431182
Mean0.63669553
Median Absolute Deviation (MAD)0
Skewness1.4045932
Sum19707
Variance1.0576602
MonotonicityNot monotonic
2024-07-30T20:13:23.516425image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 21800
70.4%
2 8190
 
26.5%
3 680
 
2.2%
4 155
 
0.5%
5 52
 
0.2%
7 35
 
0.1%
6 23
 
0.1%
1 16
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 21800
70.4%
1 16
 
0.1%
2 8190
 
26.5%
3 680
 
2.2%
4 155
 
0.5%
5 52
 
0.2%
6 23
 
0.1%
7 35
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 35
 
0.1%
6 23
 
0.1%
5 52
 
0.2%
4 155
 
0.5%
3 680
 
2.2%
2 8190
 
26.5%
1 16
 
0.1%
0 21800
70.4%

pay_3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56826699
Minimum0
Maximum8
Zeros22831
Zeros (%)73.8%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:23.633357image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.00889
Coefficient of variation (CV)1.7753802
Kurtosis4.2432039
Mean0.56826699
Median Absolute Deviation (MAD)0
Skewness1.7901902
Sum17589
Variance1.0178589
MonotonicityNot monotonic
2024-07-30T20:13:23.754487image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 22831
73.8%
2 7349
 
23.7%
3 482
 
1.6%
4 150
 
0.5%
7 54
 
0.2%
6 40
 
0.1%
5 33
 
0.1%
8 7
 
< 0.1%
1 6
 
< 0.1%
ValueCountFrequency (%)
0 22831
73.8%
1 6
 
< 0.1%
2 7349
 
23.7%
3 482
 
1.6%
4 150
 
0.5%
5 33
 
0.1%
6 40
 
0.1%
7 54
 
0.2%
8 7
 
< 0.1%
ValueCountFrequency (%)
8 7
 
< 0.1%
7 54
 
0.2%
6 40
 
0.1%
5 33
 
0.1%
4 150
 
0.5%
3 482
 
1.6%
2 7349
 
23.7%
1 6
 
< 0.1%
0 22831
73.8%

pay_4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46853192
Minimum0
Maximum8
Zeros24418
Zeros (%)78.9%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:23.872497image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.97689621
Coefficient of variation (CV)2.0850153
Kurtosis8.0622151
Mean0.46853192
Median Absolute Deviation (MAD)0
Skewness2.407194
Sum14502
Variance0.9543262
MonotonicityNot monotonic
2024-07-30T20:13:23.997398image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 24418
78.9%
2 5838
 
18.9%
3 356
 
1.2%
4 160
 
0.5%
7 110
 
0.4%
5 58
 
0.2%
6 5
 
< 0.1%
1 4
 
< 0.1%
8 3
 
< 0.1%
ValueCountFrequency (%)
0 24418
78.9%
1 4
 
< 0.1%
2 5838
 
18.9%
3 356
 
1.2%
4 160
 
0.5%
5 58
 
0.2%
6 5
 
< 0.1%
7 110
 
0.4%
8 3
 
< 0.1%
ValueCountFrequency (%)
8 3
 
< 0.1%
7 110
 
0.4%
6 5
 
< 0.1%
5 58
 
0.2%
4 160
 
0.5%
3 356
 
1.2%
2 5838
 
18.9%
1 4
 
< 0.1%
0 24418
78.9%

pay_5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41199276
Minimum0
Maximum8
Zeros25294
Zeros (%)81.7%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:24.142839image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.94151052
Coefficient of variation (CV)2.2852599
Kurtosis9.868474
Mean0.41199276
Median Absolute Deviation (MAD)0
Skewness2.6919562
Sum12752
Variance0.88644206
MonotonicityNot monotonic
2024-07-30T20:13:24.309548image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 25294
81.7%
2 4931
 
15.9%
3 402
 
1.3%
4 172
 
0.6%
7 109
 
0.4%
5 35
 
0.1%
6 7
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 25294
81.7%
2 4931
 
15.9%
3 402
 
1.3%
4 172
 
0.6%
5 35
 
0.1%
6 7
 
< 0.1%
7 109
 
0.4%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 109
 
0.4%
6 7
 
< 0.1%
5 35
 
0.1%
4 172
 
0.6%
3 402
 
1.3%
2 4931
 
15.9%
0 25294
81.7%

pay_6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40986043
Minimum0
Maximum8
Zeros25237
Zeros (%)81.5%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:24.434572image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.92639717
Coefficient of variation (CV)2.2602747
Kurtosis9.6047156
Mean0.40986043
Median Absolute Deviation (MAD)0
Skewness2.6397862
Sum12686
Variance0.85821171
MonotonicityNot monotonic
2024-07-30T20:13:24.576141image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 25237
81.5%
2 5085
 
16.4%
3 377
 
1.2%
4 99
 
0.3%
7 81
 
0.3%
6 45
 
0.1%
5 24
 
0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
0 25237
81.5%
2 5085
 
16.4%
3 377
 
1.2%
4 99
 
0.3%
5 24
 
0.1%
6 45
 
0.1%
7 81
 
0.3%
8 4
 
< 0.1%
ValueCountFrequency (%)
8 4
 
< 0.1%
7 81
 
0.3%
6 45
 
0.1%
5 24
 
0.1%
4 99
 
0.3%
3 377
 
1.2%
2 5085
 
16.4%
0 25237
81.5%

bill_amt1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17193
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50803.231
Minimum0
Maximum746814
Zeros1827
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:24.748102image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16305
median25420
Q364658.75
95-th percentile192950.5
Maximum746814
Range746814
Interquartile range (IQR)58353.75

Descriptive statistics

Standard deviation70331.547
Coefficient of variation (CV)1.3843912
Kurtosis10.150734
Mean50803.231
Median Absolute Deviation (MAD)23053
Skewness2.7431495
Sum1.5724616 × 109
Variance4.9465264 × 109
MonotonicityNot monotonic
2024-07-30T20:13:24.915202image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1827
 
5.9%
390 233
 
0.8%
780 99
 
0.3%
2400 74
 
0.2%
1050 58
 
0.2%
316 54
 
0.2%
396 43
 
0.1%
326 42
 
0.1%
600 37
 
0.1%
1650 25
 
0.1%
Other values (17183) 28460
91.9%
ValueCountFrequency (%)
0 1827
5.9%
3 2
 
< 0.1%
8 1
 
< 0.1%
9 5
 
< 0.1%
27 1
 
< 0.1%
37 1
 
< 0.1%
38 1
 
< 0.1%
47 1
 
< 0.1%
50 1
 
< 0.1%
54 1
 
< 0.1%
ValueCountFrequency (%)
746814 1
 
< 0.1%
613860 3
< 0.1%
610723 2
 
< 0.1%
589654 1
 
< 0.1%
581775 2
 
< 0.1%
581319 3
< 0.1%
564757 1
 
< 0.1%
563892 1
 
< 0.1%
562326 5
< 0.1%
548551 2
 
< 0.1%

bill_amt2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16808
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49322.664
Minimum0
Maximum581775
Zeros2179
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:25.076145image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16089.5
median24838
Q362197
95-th percentile187658
Maximum581775
Range581775
Interquartile range (IQR)56107.5

Descriptive statistics

Standard deviation68114.785
Coefficient of variation (CV)1.3810038
Kurtosis9.9001775
Mean49322.664
Median Absolute Deviation (MAD)22978
Skewness2.7260062
Sum1.5266351 × 109
Variance4.639624 × 109
MonotonicityNot monotonic
2024-07-30T20:13:25.234420image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2179
 
7.0%
390 222
 
0.7%
780 100
 
0.3%
2400 83
 
0.3%
326 70
 
0.2%
316 62
 
0.2%
1050 56
 
0.2%
396 52
 
0.2%
600 35
 
0.1%
2500 29
 
0.1%
Other values (16798) 28064
90.7%
ValueCountFrequency (%)
0 2179
7.0%
3 2
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
11 1
 
< 0.1%
15 1
 
< 0.1%
26 2
 
< 0.1%
28 1
 
< 0.1%
36 1
 
< 0.1%
ValueCountFrequency (%)
581775 1
 
< 0.1%
572834 1
 
< 0.1%
572677 2
 
< 0.1%
569577 1
 
< 0.1%
555086 2
 
< 0.1%
552144 3
< 0.1%
546741 2
 
< 0.1%
539418 2
 
< 0.1%
535509 2
 
< 0.1%
534289 5
< 0.1%

bill_amt3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16523
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47315.515
Minimum0
Maximum578971
Zeros2526
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:25.420167image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15978
median23675.5
Q359529
95-th percentile181490
Maximum578971
Range578971
Interquartile range (IQR)53551

Descriptive statistics

Standard deviation66037.724
Coefficient of variation (CV)1.3956886
Kurtosis10.427358
Mean47315.515
Median Absolute Deviation (MAD)22099.5
Skewness2.7966136
Sum1.4645098 × 109
Variance4.3609809 × 109
MonotonicityNot monotonic
2024-07-30T20:13:25.596067image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2526
 
8.2%
390 270
 
0.9%
2400 74
 
0.2%
780 74
 
0.2%
326 65
 
0.2%
316 62
 
0.2%
1050 49
 
0.2%
396 40
 
0.1%
600 37
 
0.1%
416 30
 
0.1%
Other values (16513) 27725
89.6%
ValueCountFrequency (%)
0 2526
8.2%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 4
 
< 0.1%
7 1
 
< 0.1%
11 1
 
< 0.1%
15 1
 
< 0.1%
23 1
 
< 0.1%
26 1
 
< 0.1%
34 1
 
< 0.1%
ValueCountFrequency (%)
578971 4
< 0.1%
577957 1
 
< 0.1%
572677 1
 
< 0.1%
548020 2
 
< 0.1%
537543 5
< 0.1%
535509 2
 
< 0.1%
523423 3
< 0.1%
519267 2
 
< 0.1%
517746 1
 
< 0.1%
499936 2
 
< 0.1%

bill_amt4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16166
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43861.488
Minimum0
Maximum628699
Zeros2804
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:25.848277image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15429.25
median21074
Q354067.5
95-th percentile167940.1
Maximum628699
Range628699
Interquartile range (IQR)48638.25

Descriptive statistics

Standard deviation62066.709
Coefficient of variation (CV)1.4150616
Kurtosis11.491819
Mean43861.488
Median Absolute Deviation (MAD)19664.5
Skewness2.9017798
Sum1.3576008 × 109
Variance3.8522763 × 109
MonotonicityNot monotonic
2024-07-30T20:13:26.003214image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2804
 
9.1%
390 217
 
0.7%
780 103
 
0.3%
316 73
 
0.2%
2400 73
 
0.2%
326 58
 
0.2%
1050 56
 
0.2%
600 44
 
0.1%
416 39
 
0.1%
1261 37
 
0.1%
Other values (16156) 27448
88.7%
ValueCountFrequency (%)
0 2804
9.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
10 1
 
< 0.1%
11 2
 
< 0.1%
14 1
 
< 0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
30 1
 
< 0.1%
37 1
 
< 0.1%
ValueCountFrequency (%)
628699 1
 
< 0.1%
616836 1
 
< 0.1%
563543 1
 
< 0.1%
548020 2
 
< 0.1%
541019 5
< 0.1%
530672 2
 
< 0.1%
518741 2
 
< 0.1%
514249 2
 
< 0.1%
505507 2
 
< 0.1%
504929 5
< 0.1%

bill_amt5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15764
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40687.128
Minimum0
Maximum587067
Zeros3129
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:26.157459image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14192
median19704.5
Q349794
95-th percentile157116.65
Maximum587067
Range587067
Interquartile range (IQR)45602

Descriptive statistics

Standard deviation58379.932
Coefficient of variation (CV)1.4348502
Kurtosis11.778769
Mean40687.128
Median Absolute Deviation (MAD)18417.5
Skewness2.9266215
Sum1.259348 × 109
Variance3.4082164 × 109
MonotonicityNot monotonic
2024-07-30T20:13:26.320870image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3129
 
10.1%
390 218
 
0.7%
780 116
 
0.4%
316 83
 
0.3%
2400 74
 
0.2%
326 60
 
0.2%
1050 54
 
0.2%
150 51
 
0.2%
300 45
 
0.1%
396 39
 
0.1%
Other values (15754) 27083
87.5%
ValueCountFrequency (%)
0 3129
10.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
11 1
 
< 0.1%
15 1
 
< 0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
20 2
 
< 0.1%
25 1
 
< 0.1%
ValueCountFrequency (%)
587067 1
 
< 0.1%
547880 3
< 0.1%
530672 2
 
< 0.1%
516139 2
 
< 0.1%
514114 1
 
< 0.1%
508213 2
 
< 0.1%
503914 1
 
< 0.1%
501474 5
< 0.1%
486721 2
 
< 0.1%
484612 2
 
< 0.1%

bill_amt6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15420
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39096.694
Minimum0
Maximum527711
Zeros3717
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:26.516191image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12946
median19225
Q348760.25
95-th percentile153202.65
Maximum527711
Range527711
Interquartile range (IQR)45814.25

Descriptive statistics

Standard deviation56688.472
Coefficient of variation (CV)1.4499556
Kurtosis11.395759
Mean39096.694
Median Absolute Deviation (MAD)18239
Skewness2.8875721
Sum1.2101209 × 109
Variance3.2135828 × 109
MonotonicityNot monotonic
2024-07-30T20:13:26.730692image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3717
 
12.0%
390 197
 
0.6%
780 93
 
0.3%
150 85
 
0.3%
316 79
 
0.3%
326 59
 
0.2%
2400 56
 
0.2%
1050 54
 
0.2%
416 38
 
0.1%
396 36
 
0.1%
Other values (15410) 26538
85.7%
ValueCountFrequency (%)
0 3717
12.0%
2 1
 
< 0.1%
5 1
 
< 0.1%
8 1
 
< 0.1%
11 1
 
< 0.1%
18 1
 
< 0.1%
22 1
 
< 0.1%
25 1
 
< 0.1%
30 1
 
< 0.1%
37 1
 
< 0.1%
ValueCountFrequency (%)
527711 1
 
< 0.1%
527566 1
 
< 0.1%
514975 2
 
< 0.1%
499100 1
 
< 0.1%
498316 2
 
< 0.1%
496801 2
 
< 0.1%
478034 5
< 0.1%
472480 2
 
< 0.1%
469961 1
 
< 0.1%
468305 2
 
< 0.1%

pay_amt1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6433
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5185.7766
Minimum0
Maximum873552
Zeros6253
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:26.895997image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1650
median2000
Q34840
95-th percentile16054.05
Maximum873552
Range873552
Interquartile range (IQR)4190

Descriptive statistics

Standard deviation15491.173
Coefficient of variation (CV)2.9872428
Kurtosis454.22149
Mean5185.7766
Median Absolute Deviation (MAD)1999
Skewness14.891596
Sum1.6051016 × 108
Variance2.3997645 × 108
MonotonicityNot monotonic
2024-07-30T20:13:27.047831image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6253
 
20.2%
2000 1617
 
5.2%
3000 1005
 
3.2%
5000 736
 
2.4%
1500 622
 
2.0%
4000 430
 
1.4%
1000 430
 
1.4%
2500 350
 
1.1%
10000 332
 
1.1%
6000 270
 
0.9%
Other values (6423) 18907
61.1%
ValueCountFrequency (%)
0 6253
20.2%
1 13
 
< 0.1%
2 15
 
< 0.1%
3 16
 
0.1%
4 23
 
0.1%
5 18
 
0.1%
6 23
 
0.1%
7 9
 
< 0.1%
8 7
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
873552 1
< 0.1%
423903 1
< 0.1%
405016 1
< 0.1%
368199 1
< 0.1%
323014 1
< 0.1%
304815 1
< 0.1%
300039 1
< 0.1%
300000 1
< 0.1%
298887 1
< 0.1%
272817 1
< 0.1%

pay_amt2
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6472
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5180.5418
Minimum0
Maximum1215471
Zeros6068
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:27.199207image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1659.5
median2000
Q34500
95-th percentile15887.35
Maximum1215471
Range1215471
Interquartile range (IQR)3840.5

Descriptive statistics

Standard deviation18029.116
Coefficient of variation (CV)3.4801603
Kurtosis830.14934
Mean5180.5418
Median Absolute Deviation (MAD)1842
Skewness20.071741
Sum1.6034813 × 108
Variance3.2504903 × 108
MonotonicityNot monotonic
2024-07-30T20:13:27.374104image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6068
 
19.6%
2000 1491
 
4.8%
3000 946
 
3.1%
5000 712
 
2.3%
1000 676
 
2.2%
1500 662
 
2.1%
4000 422
 
1.4%
2500 287
 
0.9%
10000 286
 
0.9%
6000 279
 
0.9%
Other values (6462) 19123
61.8%
ValueCountFrequency (%)
0 6068
19.6%
1 9
 
< 0.1%
2 28
 
0.1%
3 19
 
0.1%
4 17
 
0.1%
5 38
 
0.1%
6 9
 
< 0.1%
7 19
 
0.1%
8 13
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
1215471 1
 
< 0.1%
580464 1
 
< 0.1%
415552 1
 
< 0.1%
401003 1
 
< 0.1%
388126 1
 
< 0.1%
385228 1
 
< 0.1%
384986 1
 
< 0.1%
368199 1
 
< 0.1%
358689 8
< 0.1%
340000 1
 
< 0.1%

pay_amt3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6130
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4763.2548
Minimum0
Maximum889043
Zeros6493
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:27.563360image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1380
median1603
Q34000
95-th percentile15066
Maximum889043
Range889043
Interquartile range (IQR)3620

Descriptive statistics

Standard deviation16135.374
Coefficient of variation (CV)3.3874682
Kurtosis481.00156
Mean4763.2548
Median Absolute Deviation (MAD)1603
Skewness15.910021
Sum1.4743226 × 108
Variance2.603503 × 108
MonotonicityNot monotonic
2024-07-30T20:13:27.732535image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6493
 
21.0%
2000 1433
 
4.6%
1000 1322
 
4.3%
3000 964
 
3.1%
5000 689
 
2.2%
1500 624
 
2.0%
4000 376
 
1.2%
1200 292
 
0.9%
10000 266
 
0.9%
1300 246
 
0.8%
Other values (6120) 18247
59.0%
ValueCountFrequency (%)
0 6493
21.0%
1 16
 
0.1%
2 18
 
0.1%
3 11
 
< 0.1%
4 19
 
0.1%
5 24
 
0.1%
6 19
 
0.1%
7 13
 
< 0.1%
8 13
 
< 0.1%
9 10
 
< 0.1%
ValueCountFrequency (%)
889043 1
< 0.1%
508229 2
< 0.1%
417588 1
< 0.1%
400972 1
< 0.1%
397092 1
< 0.1%
380478 1
< 0.1%
371718 1
< 0.1%
349395 1
< 0.1%
338394 1
< 0.1%
310852 1
< 0.1%

pay_amt4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5684
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4360.1245
Minimum0
Maximum621000
Zeros6870
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:27.904478image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1234
median1369
Q33600
95-th percentile15000
Maximum621000
Range621000
Interquartile range (IQR)3366

Descriptive statistics

Standard deviation14053.267
Coefficient of variation (CV)3.2231343
Kurtosis332.71134
Mean4360.1245
Median Absolute Deviation (MAD)1369
Skewness13.659744
Sum1.3495457 × 108
Variance1.974943 × 108
MonotonicityNot monotonic
2024-07-30T20:13:28.062062image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6870
 
22.2%
1000 1690
 
5.5%
2000 1352
 
4.4%
3000 908
 
2.9%
5000 732
 
2.4%
1500 603
 
1.9%
4000 387
 
1.3%
10000 291
 
0.9%
500 277
 
0.9%
2500 268
 
0.9%
Other values (5674) 17574
56.8%
ValueCountFrequency (%)
0 6870
22.2%
1 22
 
0.1%
2 19
 
0.1%
3 10
 
< 0.1%
4 18
 
0.1%
5 18
 
0.1%
6 21
 
0.1%
7 10
 
< 0.1%
8 9
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
621000 1
< 0.1%
528897 1
< 0.1%
432130 2
< 0.1%
330982 1
< 0.1%
320008 1
< 0.1%
292962 1
< 0.1%
281225 1
< 0.1%
280695 1
< 0.1%
265852 1
< 0.1%
250144 1
< 0.1%

pay_amt5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5629
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4314.4813
Minimum0
Maximum426529
Zeros7098
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:28.308647image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1200
median1423
Q33625.25
95-th percentile14314.4
Maximum426529
Range426529
Interquartile range (IQR)3425.25

Descriptive statistics

Standard deviation13637.794
Coefficient of variation (CV)3.1609348
Kurtosis204.20829
Mean4314.4813
Median Absolute Deviation (MAD)1423
Skewness11.570032
Sum1.3354182 × 108
Variance1.8598942 × 108
MonotonicityNot monotonic
2024-07-30T20:13:28.455391image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7098
22.9%
1000 1618
 
5.2%
2000 1455
 
4.7%
3000 1010
 
3.3%
5000 753
 
2.4%
1500 515
 
1.7%
4000 421
 
1.4%
10000 309
 
1.0%
500 274
 
0.9%
2500 260
 
0.8%
Other values (5619) 17239
55.7%
ValueCountFrequency (%)
0 7098
22.9%
1 16
 
0.1%
2 9
 
< 0.1%
3 6
 
< 0.1%
4 15
 
< 0.1%
5 6
 
< 0.1%
6 5
 
< 0.1%
7 6
 
< 0.1%
8 11
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
426529 1
< 0.1%
417990 1
< 0.1%
388071 1
< 0.1%
332000 1
< 0.1%
326889 1
< 0.1%
303512 1
< 0.1%
302823 1
< 0.1%
287982 2
< 0.1%
284069 1
< 0.1%
279000 2
< 0.1%

pay_amt6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5695
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4587.0264
Minimum0
Maximum528666
Zeros7678
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size483.6 KiB
2024-07-30T20:13:28.603075image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median1302
Q33500
95-th percentile15000
Maximum528666
Range528666
Interquartile range (IQR)3494

Descriptive statistics

Standard deviation16050.361
Coefficient of variation (CV)3.4990776
Kurtosis201.62079
Mean4587.0264
Median Absolute Deviation (MAD)1302
Skewness11.506735
Sum1.4197764 × 108
Variance2.5761409 × 108
MonotonicityNot monotonic
2024-07-30T20:13:28.763365image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7678
24.8%
1000 1508
 
4.9%
2000 1430
 
4.6%
3000 967
 
3.1%
5000 776
 
2.5%
1500 622
 
2.0%
4000 398
 
1.3%
10000 285
 
0.9%
500 285
 
0.9%
2500 238
 
0.8%
Other values (5685) 16765
54.2%
ValueCountFrequency (%)
0 7678
24.8%
1 19
 
0.1%
2 7
 
< 0.1%
3 11
 
< 0.1%
4 10
 
< 0.1%
5 10
 
< 0.1%
6 9
 
< 0.1%
7 5
 
< 0.1%
8 7
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
528666 1
< 0.1%
527143 1
< 0.1%
422000 1
< 0.1%
403500 1
< 0.1%
377000 1
< 0.1%
372495 1
< 0.1%
351282 1
< 0.1%
308000 1
< 0.1%
287982 1
< 0.1%
280000 1
< 0.1%

gb_flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
16685 
1
14267 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30952
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 16685
53.9%
1 14267
46.1%

Length

2024-07-30T20:13:28.906447image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-30T20:13:29.009248image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 16685
53.9%
1 14267
46.1%

Most occurring characters

ValueCountFrequency (%)
0 16685
53.9%
1 14267
46.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30952
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16685
53.9%
1 14267
46.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30952
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16685
53.9%
1 14267
46.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30952
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16685
53.9%
1 14267
46.1%

Interactions

2024-07-30T20:13:18.472315image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:31.722508image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:34.258141image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:36.629061image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:38.856057image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:41.360445image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:44.038533image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:46.419783image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:48.938755image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:51.470979image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:54.102636image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:56.615923image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:58.904237image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:01.985507image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:04.159099image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:06.599016image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:08.833161image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:11.468526image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:13.875780image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:16.172912image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:18.588163image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:31.860264image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:34.377134image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:36.750421image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:39.060230image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:41.515327image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:44.178422image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:46.548457image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:49.073744image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:51.592354image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:54.222418image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:56.733869image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:59.028919image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:02.101043image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:04.380129image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:06.734676image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:09.048101image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:11.588588image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:14.022780image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:16.283968image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:18.698543image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:31.979581image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:34.487986image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:36.854049image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:39.166774image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:41.650594image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:44.298754image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:46.796138image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:49.197052image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:51.701824image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:54.338360image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:56.844147image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:59.140056image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:02.204413image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:04.523413image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:06.851623image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:09.153279image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:11.695268image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:14.134205image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:16.386005image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:18.810483image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:32.123822image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:34.639410image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:36.969692image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:39.282416image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:41.810285image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:44.413295image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:46.924562image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:49.318301image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:51.822912image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:54.461747image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:56.963278image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:59.267071image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:02.320297image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:04.649558image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:06.974193image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:09.275981image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:11.816284image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:14.252085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:16.507942image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:18.937910image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:32.269072image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:34.773309image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:37.084289image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:39.397472image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:41.946270image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:44.527682image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:47.042840image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:49.448364image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:51.945209image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:54.583784image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:57.079109image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:59.386265image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:02.434198image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:04.806731image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:07.085149image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:09.393215image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:11.928298image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:14.368540image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:16.625559image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:19.056681image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:32.395286image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:34.901822image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:37.198604image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:39.515467image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:42.067189image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:44.644159image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:47.157735image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:49.579098image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:52.073619image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:54.787207image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:57.196184image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:59.621433image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:02.549060image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:04.929759image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:07.194520image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:09.531505image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:12.057483image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:14.488853image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:16.749322image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:19.222460image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:32.524501image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:35.021451image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:37.311030image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:39.632138image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:42.182660image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:44.759697image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:47.273446image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:49.699177image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:52.225619image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:54.946048image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:57.312042image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:59.755878image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:02.662435image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:05.043335image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:07.305270image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:09.686322image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:12.185207image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:14.609043image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:16.862487image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:19.411255image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:32.660534image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:35.136899image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:37.424362image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:39.756474image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:42.328674image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:44.877348image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:47.389347image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:49.816092image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:52.369368image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:55.075409image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:57.434468image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:59.884483image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:02.776383image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:05.158737image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:07.416230image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:09.802390image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:12.306470image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:14.737802image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:16.974195image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:19.540253image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:32.823919image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:35.252548image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:37.539526image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:39.883986image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:42.477005image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:44.992606image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:47.505846image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:49.933955image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:52.498744image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:55.310819image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:57.554124image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:00.041876image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:02.892376image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:05.271479image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:07.532984image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:09.916031image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:12.430131image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:14.883260image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:17.086051image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:19.650806image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:32.951074image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:35.360956image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:37.656357image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:40.007387image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:42.610387image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:45.115109image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:47.631690image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:50.050879image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:52.619115image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:55.430573image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:57.678667image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:00.181402image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:03.004220image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:05.381581image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:07.653305image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:10.030231image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:12.557487image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:15.002472image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:17.194370image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:19.760214image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:33.073076image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:35.594129image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:37.766392image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:40.144503image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:42.741861image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:45.249542image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:47.760570image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:50.166097image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:52.729293image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:55.538439image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:57.786631image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:00.299053image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:03.110296image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:05.498277image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:07.779760image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:10.140679image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:12.672795image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:15.116014image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:17.299050image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:19.869549image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:33.192366image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:35.699490image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:37.878428image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:40.314121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:42.860142image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:45.380648image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:47.883327image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:50.280493image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:52.992354image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:55.654394image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:57.908095image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:00.439507image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:03.216168image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:05.605280image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:07.901074image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:10.260116image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:12.800503image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:15.228679image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:17.418375image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:19.980410image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:33.314180image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:35.811851image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:38.002344image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:40.441482image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:43.107056image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:45.510473image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:48.010195image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:50.392685image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:53.136401image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:55.774138image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:58.015187image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:00.591454image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:03.326119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:05.711266image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:08.005290image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:10.382041image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:12.919423image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:15.336368image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:17.527150image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:20.106126image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:33.433440image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:35.916426image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:38.115281image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:40.552425image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:43.230448image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:45.621558image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:48.137120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:50.631433image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:53.253076image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:55.887472image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:58.134003image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:00.828572image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:03.431522image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:05.818412image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:08.107330image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:10.507343image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:13.027024image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:15.443829image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:17.632051image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:20.216080image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:33.545316image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:36.022532image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:38.220115image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:40.659636image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:43.340989image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:45.729187image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:48.265382image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:50.744557image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:53.370427image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:55.990672image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:58.236276image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:01.010403image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:03.535192image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:05.920060image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:08.206215image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:10.631315image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:13.129271image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:15.548492image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:17.729858image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:20.312651image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:33.661898image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:36.122463image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:38.324118image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:40.762188image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:43.450414image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:45.832084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:48.380209image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:50.853072image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:53.532703image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:56.094265image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:58.349194image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:01.254473image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:03.632678image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:06.015538image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:08.299453image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:10.742446image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:13.225183image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:15.646942image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:17.827335image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:20.420411image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:33.784182image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:36.231169image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:38.434144image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:40.877394image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:43.575639image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:45.957355image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:48.493140image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:50.968142image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:53.662669image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:56.204112image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:58.460924image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:01.531665image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:03.749156image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:06.125499image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:08.405549image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:10.897388image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:13.331324image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:15.758470image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:17.938853image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:20.520052image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:33.908650image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:36.327384image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:38.536027image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:41.003175image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:43.687732image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:46.074253image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:48.596097image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:51.091050image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:53.777057image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:56.305516image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:58.565199image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:01.675956image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:03.848841image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:06.223130image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:08.499389image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:11.014121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:13.426408image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:15.859103image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:18.167539image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:20.635192image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:34.026388image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:36.432427image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:38.647865image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:41.140604image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:43.804100image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:46.194561image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:48.710279image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:51.240459image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:53.894452image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:56.412422image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:58.679812image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:01.782908image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:03.958138image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:06.347255image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:08.606175image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:11.136200image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:13.628487image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:15.966497image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:18.269392image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:20.740296image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:34.133679image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:36.529635image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:38.750716image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:41.248908image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:43.915657image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:46.299287image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:48.815995image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:51.359004image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:53.997282image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:56.512644image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:12:58.780173image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:01.883937image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:04.056369image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:06.469236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:08.715173image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:11.318302image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:13.744288image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:16.068162image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T20:13:18.364442image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-07-30T20:13:29.116212image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
agebill_amt1bill_amt2bill_amt3bill_amt4bill_amt5bill_amt6educationgb_flaglimit_balmarriagepay_1pay_2pay_3pay_4pay_5pay_6pay_amt1pay_amt2pay_amt3pay_amt4pay_amt5pay_amt6sex
age1.0000.0320.0320.0340.0310.0290.0320.1630.1030.1680.356-0.020-0.015-0.015-0.006-0.020-0.0280.0530.0670.0530.0530.0570.0650.099
bill_amt10.0321.0000.9260.8790.8270.7840.7420.0450.1200.1720.0350.0720.0980.0530.0570.0680.0590.4330.4310.4120.4160.4000.3850.046
bill_amt20.0320.9261.0000.9240.8670.8160.7730.0450.1380.1610.0460.0930.0940.0870.0770.0860.0740.5400.4470.4350.4280.4230.4030.046
bill_amt30.0340.8790.9241.0000.9180.8600.8110.0450.1510.1640.0490.1020.1040.0850.1090.1090.0930.4760.5530.4490.4480.4430.4250.049
bill_amt40.0310.8270.8670.9181.0000.9140.8560.0430.1590.1680.0370.1100.1160.1080.1240.1480.1260.4480.4940.5590.4620.4650.4450.040
bill_amt50.0290.7840.8160.8600.9141.0000.9140.0430.1620.1740.0430.1220.1280.1230.1510.1700.1700.4160.4600.4950.5730.4830.4750.042
bill_amt60.0320.7420.7730.8110.8560.9141.0000.0440.1690.1800.0380.1290.1310.1290.1600.1870.1780.3930.4310.4690.5150.6000.4960.040
education0.1630.0450.0450.0450.0430.0430.0441.0000.1590.1620.1400.0580.0570.0540.0580.0530.0510.0090.0000.0350.0070.0150.0260.018
gb_flag0.1030.1200.1380.1510.1590.1620.1690.1591.0000.3650.0190.4570.4170.4110.4290.4110.3860.0370.0350.0360.0410.0660.0630.067
limit_bal0.1680.1720.1610.1640.1680.1740.1800.1620.3651.0000.069-0.241-0.237-0.232-0.251-0.223-0.2180.3430.3420.3580.3490.3680.3870.078
marriage0.3560.0350.0460.0490.0370.0430.0380.1400.0190.0691.0000.0000.0180.0120.0110.0100.0100.0360.0350.0350.0340.0060.0000.037
pay_1-0.0200.0720.0930.1020.1100.1220.1290.0580.457-0.2410.0001.0000.6080.3970.3620.3400.304-0.269-0.152-0.136-0.110-0.078-0.1050.044
pay_2-0.0150.0980.0940.1040.1160.1280.1310.0570.417-0.2370.0180.6081.0000.5680.4350.3990.359-0.346-0.169-0.118-0.099-0.069-0.0890.049
pay_3-0.0150.0530.0870.0850.1080.1230.1290.0540.411-0.2320.0120.3970.5681.0000.6110.4660.417-0.029-0.337-0.160-0.109-0.077-0.0980.048
pay_4-0.0060.0570.0770.1090.1240.1510.1600.0580.429-0.2510.0110.3620.4350.6111.0000.6840.512-0.086-0.034-0.301-0.136-0.083-0.0940.051
pay_5-0.0200.0680.0860.1090.1480.1700.1870.0530.411-0.2230.0100.3400.3990.4660.6841.0000.687-0.103-0.069-0.038-0.242-0.090-0.0790.048
pay_6-0.0280.0590.0740.0930.1260.1700.1780.0510.386-0.2180.0100.3040.3590.4170.5120.6871.000-0.089-0.093-0.056-0.005-0.232-0.1070.045
pay_amt10.0530.4330.5400.4760.4480.4160.3930.0090.0370.3430.036-0.269-0.346-0.029-0.086-0.103-0.0891.0000.3940.4640.4280.4190.4090.000
pay_amt20.0670.4310.4470.5530.4940.4600.4310.0000.0350.3420.035-0.152-0.169-0.337-0.034-0.069-0.0930.3941.0000.4250.4760.4420.4450.016
pay_amt30.0530.4120.4350.4490.5590.4950.4690.0350.0360.3580.035-0.136-0.118-0.160-0.301-0.038-0.0560.4640.4251.0000.4330.4940.4660.011
pay_amt40.0530.4160.4280.4480.4620.5730.5150.0070.0410.3490.034-0.110-0.099-0.109-0.136-0.242-0.0050.4280.4760.4331.0000.4480.5060.009
pay_amt50.0570.4000.4230.4430.4650.4830.6000.0150.0660.3680.006-0.078-0.069-0.077-0.083-0.090-0.2320.4190.4420.4940.4481.0000.4810.009
pay_amt60.0650.3850.4030.4250.4450.4750.4960.0260.0630.3870.000-0.105-0.089-0.098-0.094-0.079-0.1070.4090.4450.4660.5060.4811.0000.003
sex0.0990.0460.0460.0490.0400.0420.0400.0180.0670.0780.0370.0440.0490.0480.0510.0480.0450.0000.0160.0110.0090.0090.0031.000

Missing values

2024-07-30T20:13:20.919773image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-30T20:13:21.302572image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

limit_balsexeducationmarriageagepay_1pay_2pay_3pay_4pay_5pay_6bill_amt1bill_amt2bill_amt3bill_amt4bill_amt5bill_amt6pay_amt1pay_amt2pay_amt3pay_amt4pay_amt5pay_amt6gb_flag
id
120000WanitaSMALajang3900223212241.016020.016457.020906.020289.020407.0400010004750060001
2100000PriaSMALajang490000001440.00.00.00.00.00.00000000
380000WanitaPascasarjanaLajang2620022237097.038174.040550.041577.041595.043264.02000300020001000250010001
4280000WanitaSarjanaMenikah31000222127609.076057.075377.068277.072042.065921.06000600004800022260
5130000PriaSarjanaMenikah53222000109994.0125138.0114344.096995.0100086.092344.0171001740005000400040001
6340000WanitaSarjanaLajang47000000196112.0183453.0167592.0162628.0142779.0139548.06446620757584853494046000
7260000PriaSMAMenikah490000008654.015260.06790.010070.0585.0435.0153286914100985854354270
820000WanitaSMALajang5400000016120.017160.018083.015096.015396.015720.013081231124454456515001
960000PriaSarjanaMenikah3500022210560.011312.013050.012578.014008.013179.0120019000160005001
1080000WanitaSMALajang2714320084718.082861.080581.051537.051381.048866.00001790189017401
limit_balsexeducationmarriageagepay_1pay_2pay_3pay_4pay_5pay_6bill_amt1bill_amt2bill_amt3bill_amt4bill_amt5bill_amt6pay_amt1pay_amt2pay_amt3pay_amt4pay_amt5pay_amt6gb_flag
id
30943100000PriaSarjanaLajang2800200082238.0100753.093209.089813.055359.032227.0200000350130215011534080
3094450000PriaSarjanaLajang3612222219649.020526.019909.022096.022438.021941.01500025311000020001
30945150000WanitaSarjanaMenikah29100022123248.0126311.0130388.0138246.0140513.0118464.0500060001050050151731021
30946180000PriaPascasarjanaLajang290000001073.03191.010693.011285.013863.05646.032061069612913877110777000
30947280000PriaSarjanaLajang30000000261602.0267238.0273951.0262720.0130384.0102181.01000011075897251821000040000
30948100000WanitaPascasarjanaMenikah4400000268444.071722.073525.075303.079994.078550.05000300030005900050001
30949400000WanitaPascasarjanaLajang290000005154.06016.07056.07310.09943.012849.0106011003103000300061000
30950250000WanitaSarjanaLajang2900000046948.046351.033227.033732.032245.037589.025001000030003000800000
30951270000PriaSarjanaLajang310000000.01521.00.00.00.00.0152100002000
30952400000WanitaPascasarjanaLajang2900200074829.077970.070557.071867.072137.072955.07017032002750300058001

Duplicate rows

Most frequently occurring

limit_balsexeducationmarriageagepay_1pay_2pay_3pay_4pay_5pay_6bill_amt1bill_amt2bill_amt3bill_amt4bill_amt5bill_amt6pay_amt1pay_amt2pay_amt3pay_amt4pay_amt5pay_amt6gb_flag# duplicates
6410000PriaSarjanaLajang321200009001.08322.08630.08630.07850.08150.0014000016000112
3554320000WanitaSMALajang40122220298343.0304421.0298042.0315502.0310308.0306056.01230002400001100014000111
13310000WanitaSarjanaMenikah461222227660.08689.08403.09505.08889.010263.0130601735020000110
38520000PriaSarjanaMenikah380000025705.06715.07736.08745.09394.09101.01266128712969450481110
2828140000PriaSarjanaLajang41000000144904.0143827.0141805.065352.518432.581185.0100001000050006000760003000110
110000PriaPascasarjanaLajang23020000780.0390.0390.00.0780.00.0039007800019
14720000PriaPascasarjanaLajang2412243217375.016799.021408.020779.020170.019721.004893000100019
40120000PriaSarjanaMenikah4300000012721.014102.014870.016116.016436.016781.016001300150058861061119
59020000WanitaSarjanaMenikah340002326827.07759.09618.010313.09863.09558.01200200010000050819
71030000PriaSarjanaLajang2412220027262.026545.028590.027831.028433.030257.002800010502300019